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Transformative Treatments
Author(s) -
Paul L.A.,
Healy Kieran
Publication year - 2018
Publication title -
noûs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.574
H-Index - 66
eISSN - 1468-0068
pISSN - 0029-4624
DOI - 10.1111/nous.12180
Subject(s) - transformative learning , counterfactual thinking , causal inference , selection (genetic algorithm) , identification (biology) , epistemology , selection bias , psychology , inference , cognitive psychology , sociology , social psychology , computer science , developmental psychology , medicine , artificial intelligence , econometrics , biology , mathematics , philosophy , botany , pathology
Abstract Contemporary social‐scientific research seeks to identify specific causal mechanisms for outcomes of theoretical interest. Experiments that randomize populations to treatment and control conditions are the “gold standard” for causal inference. We identify, describe, and analyze the problem posed by transformative treatments . Such treatments radically change treated individuals in a way that creates a mismatch in populations, but this mismatch is not empirically detectable at the level of counterfactual dependence. In such cases, the identification of causal pathways is underdetermined in a previously unrecognized way. Moreover, if the treatment is indeed transformative it breaks the inferential structure of the experimental design. Transformative treatments are not curiosities or “corner cases,” but are plausible mechanisms in a large class of events of theoretical interest, particularly ones where deliberate randomization is impractical and quasi‐experimental designs are sought instead. They cast long‐running debates about treatment and selection effects in a new light, and raise new methodological challenges.